A novel image retrieval technique for enhanced telemedical applications

Future direction telemedicine depends on effective usage of bandwidth due to the huge amount of data generated by high resolution medical images and expert systems, which will eventually try to replace the expert for opinions. Medical image retrieval and lossless-lossy compression will redefine the way data is sent and retrieved in telemedicine systems. This paper proposes a novel method to enable telemedicine even if an expert is not available. The proposed method combines content based image retrieval to retrieve diagnostic cases similar to the query medical image and image compression techniques to minimize the bandwidth utilization.

[1]  Jelena Kovacevic,et al.  Wavelets and Subband Coding , 2013, Prentice Hall Signal Processing Series.

[2]  Oleg Starostenko,et al.  Content-based image retrieval using wavelets , 2008 .

[3]  Koichi Niijima,et al.  Image retrieval using lifting wavelet filters , 2002 .

[4]  Antoine Geissbühler,et al.  A Review of Content{Based Image Retrieval Systems in Medical Applications { Clinical Bene(cid:12)ts and Future Directions , 2022 .

[5]  Achintya Singhal,et al.  Comparison of different wavelets for watermarking of colored images , 2011, 2011 3rd International Conference on Electronics Computer Technology.

[6]  K. Kale,et al.  Performance Analysis of Biorthogonal Wavelet Filters for Lossy Fingerprint Image Compression , 2004 .

[7]  Divya Mohandass,et al.  Lossless Compression Techniques for Medical Images In Telemedicine , 2011 .

[8]  Salim Lahmiri,et al.  Brain MRI classification using an ensemble system and LH and HL wavelet sub-bands features , 2011, 2011 IEEE Third International Workshop On Computational Intelligence In Medical Imaging.

[9]  S. Sitharama Iyengar,et al.  Medical Datamining with a New Algorithm for Feature Selection and Naive Bayesian Classifier , 2007 .

[10]  Seyed Ghorshi,et al.  An efficient lossless medical image transformation method by improving prediction model , 2010, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS.

[11]  A. Shanmugam,et al.  Implementation of Featureset Reduced Symmetric Transform in Image Retrieval Optimized for FPGA , 2010 .

[12]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  L. R. Long,et al.  Vertebra shape classification using MLP for content-based image retrieval , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[14]  Hossein Pourghassem,et al.  Content-based medical image classification using a new hierarchical merging scheme , 2008, Comput. Medical Imaging Graph..

[15]  S. Radhika,et al.  Applicability of BPN and MLP neural networks for classification of noises present in different image formats , 2011 .

[16]  Zaher Al Aghbari,et al.  Bayesian based classifier for mining image classes , 2005, IADIS AC.

[17]  E. Hoffman,et al.  Lung image database consortium: developing a resource for the medical imaging research community. , 2004, Radiology.

[18]  Anjali S. Bhalchandra,et al.  Performance Evaluation of Image Retrieval Using VQ for Compressed and Uncompressed Images , 2009, 2009 Second International Conference on Emerging Trends in Engineering & Technology.

[19]  B. Lerner,et al.  On the Classification of a Small Imbalanced Cytogenetic Image Database , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[20]  S. S. Iyengar,et al.  Medical Datamining with a New Algorithm for Feature Selection and Naive Bayesian Classifier , 2007, 10th International Conference on Information Technology (ICIT 2007).